User behavior on websites tends to be sequential in nature; actions can be ordered by the time at which they are taken, which naturally leads to sequential modeling methods.
Various methods have been proposed to predict future engagement based on users' sequences of historical interactions [1]}, [2]}, [3]}, [4]}, [5]}.
More recent works have applied various deep learning models, including recurrent neural networks (RNNs) and transformers for such sequential recommendations and obtained promising results [1]}, [2]}, [3]}, [9]}, [10]}, [11]}, [12]}.
Sequential models traditionally focus on a realtime setting, aiming to predict a user's next action or engagement from all actions leading up to that point.
